Runs on Python version 3.0.x - 3.6.x
create a virtual environment to run algorithm using anaconda if it fails to run on your local base machine.
conda create --name py36-tf python=3.6
conda activate py36-tf
Vehicle Collision Warning System is a safety system that is designed to assist drivers to avoid collisions with other vehicles and passengers through warnings and alerts. Its major role is to reduce road accidents, deaths and injuries caused by alerting the driver to the risks. In this specific project we were asked to design and develop a Vehicle Collision Warning System that uses the Vehicle Dash Cam Video Stream to help drivers through producing warning signs. This particular system tracks the movements of the vehicles and predicts their movements for two seconds ahead. If the other vehicles are predicted to enter into the Danger Area marked by the user in the next two seconds then a Warning Sign is generated to alert the driver. Here already recorded Dash Cam Videos were taken as inputs to test the system.
We used project TensorFlow which is the platform for machine learning and you only look once (YOLO) which is an object detection algorithm for real-time vehicle detection in our project for detecting the vehicles. By combining TensorFlow Yolo and other dependencies with python as a programming language, we have detected vehicles in real-time Specifically, by drawing bounding boxes around these detected vehicles, which allow us to locate where said vehicles are in a given scene. In our project, we have used the latest version of TensorFlow and version 3 of YOLO. And we also have used then the Deep Sort algorithm in our project.Deep Sort Algorithm involves tracking of the objects using Kalman Filters.By using Kalman filters we are predicting the next position of the car and using deep sort and getting the Intersection over union (IOU)value of the bounding box area. We have set the predicted IOU value to 0.8, which means that if the calculated IOU value is greater or equal to 0.8 then both bounding boxes are for one vehicle otherwise they both are different vehicles. And for the warning sign, we have drawn a red bounding box in front of the dashboard. And first, we tried to get the warning sign by predicting the next position of the bounding box of a vehicle. And the idea was if the next position touches the red bound box the system will generate a warning message. But when we tried that way the system was trying to predict the warning sign for an array of consecutive predictable bounding boxes and the system took a long time and did not work as we expected due to the amount of time which we had on our hand, we decided to use another method for the warning prediction. So what we did was without predicting the warning sign for the next predictable bounding box of a vehicle, we used the current bounding box of a vehicle for detecting the warning.
In this project, we tested our Vehicle Collision Warning System with various video inputs (1 - 2 minutes) that are taken from the Dash Cam at various locations at different time periods. We calculated the number of Correct Warnings (emergency situation and warning given), the number of False Warnings (warning given but no emergency situation) and the number of Missed Warnings (emergency situation but no warning given) for each result video of the input video. The table below shows the results we gained through testing.
| Video | Correct Warnings | False Warnings | Missed Warning |
|---|---|---|---|
| Test 1 | 7 | 0 | 0 |
| Test 2 | 9 | 1 | 0 |
| Test 3 | 18 | 0 | 1 |
| Test 4 | 12 | 0 | 0 |
| Test 5 | 16 | 0 | 0 |
Given below are some of the screenshots taken from the resulting video sets.
